PREDICT_PARADIGM: 1. Multiple Imputation
Yuwei Wang
2023-07-12
READ ME
The R script is designed for our manuscript entitled the external validation and clinical utility assessment of PREDICT breast cancer prognostic model in young women with node-negative breast cancer. This R script performed multiple imputation and run the PREDICT algorithms (version 2.2 and version 2.3) in order to calculate predicted risks for each individual in the study population. Using the script, we generated Table 2 of the manuscript.
library("readxl")
library("readr")
library("mice")
library("survival")
library("plyr")
library("tidyverse")
library("dplyr")
library("MASS")
library("VIM")
library("table1")
library("tidyr")PARADIGM patients characterisitcs
#Dataset was loaded
columns<-c("palga_nr", # patient ID
"T_LEEFT", #age at diagnosis
"incyear", #incidence year
"ER.num", #ER expression, 0 - 100%
"ER.10", #categorical ER, based on 10%, positive vs. negative
"er", #categorical ER, based on 1%, 1 vs. 0
"PR.num", #PR expression, 0 - 100%
"PR.10",#categorical PR, based on 10%, positive vs. negative
"pr", #categorical PR, based on 1%, 1 vs. 0
"HER2.cat", #categorical HER2, positive vs. negative
"her2", #categorical her2, 1 vs. 0
"TILs.cat4", #categorical TILs, absent, weak, moderate and strong
"Grade",#categorical grade, grade 1, grade 2 and grade 3
"grade", #categorical grade, 1, 2 and 3
"size", # continuous tumor size
"tumorsize.cat4", #categorical tumor size T1 to T3
"push.border", # categorical pushing order "Absent", "<25%", "25%-75%", ">75%"
"LVI", #lymphovascular invasion, 0 or 1
"HISTOLOGICAL_SUBTYPErec", #histological subtypes
"Radiotherapy", #yes or no
"Surgery.cat3", #lumpactomy, mastectomy or surgery NNO
"H.tilde", #the Nelson-aalen estimator of the baseline hazard
"delta", #death or not
"time", #followup time
"BCsubtype", #intrinsic subtype, based on ER (1%), PR (1%) and HER2
"nodes" #positive nodal status, all 0
)
PRD.final <- PRD %>%
filter(T_LEEFT >= 25) %>% #remove women diagnosed under age 25
dplyr::select(columns)## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(columns)
##
## # Now:
## data %>% select(all_of(columns))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.labels.table <- list(
variables = list(T_LEEFT = "Age (years)",
er = "ER status",
pr = "PR status",
her2 = "HER2 status",
size = "Tumor size(mm)",
grade = "Tumor grade",
LVI = "LVI",
BCsubtype = "Immunohistochemical subtype",
Surgery.cat3 = "Surgery",
Radiotherapy = "Radiotherapy"),
groups = list("", "ER stutus")
)
strata.table <- c(list(Total = PRD.final), split(PRD.final, PRD.final$er))
table1(strata.table, labels.table, groupspan=c(1, 2),
render.continuous = "MEDIAN (Q1, Q3)",
render.categorical = "FREQ (PCTnoNA%)")ER stutus |
|||
|---|---|---|---|
| Total (N=2264) |
0 (N=559) |
1 (N=1379) |
|
| Age (years) | 36.0 (33.0, 38.0) | 35.0 (32.5, 38.0) | 36.0 (34.0, 38.0) |
| ER status | |||
| 0 | 559 (28.8%) | 559 (100%) | 0 (0%) |
| 1 | 1379 (71.2%) | 0 (0%) | 1379 (100%) |
| Missing | 326 (14.4%) | 0 (0%) | 0 (0%) |
| PR status | |||
| 0 | 710 (36.7%) | 531 (95.5%) | 178 (12.9%) |
| 1 | 1225 (63.3%) | 25 (4.5%) | 1199 (87.1%) |
| Missing | 329 (14.5%) | 3 (0.5%) | 2 (0.1%) |
| HER2 status | |||
| 0 | 1614 (83.3%) | 465 (83.6%) | 1145 (83.2%) |
| 1 | 324 (16.7%) | 91 (16.4%) | 231 (16.8%) |
| Missing | 326 (14.4%) | 3 (0.5%) | 3 (0.2%) |
| Tumor size(mm) | 16.0 (12.0, 22.0) | 20.0 (15.0, 25.0) | 15.0 (12.0, 20.0) |
| Missing | 324 (14.3%) | 67 (12.0%) | 165 (12.0%) |
| Tumor grade | |||
| 1 | 367 (17.6%) | 8 (1.4%) | 337 (24.5%) |
| 2 | 800 (38.4%) | 97 (17.4%) | 653 (47.4%) |
| 3 | 917 (44.0%) | 454 (81.2%) | 387 (28.1%) |
| Missing | 180 (8.0%) | 0 (0%) | 2 (0.1%) |
| LVI | |||
| 0 | 1632 (82.4%) | 486 (86.9%) | 1118 (81.1%) |
| 1 | 349 (17.6%) | 73 (13.1%) | 260 (18.9%) |
| Missing | 283 (12.5%) | 0 (0%) | 1 (0.1%) |
| Immunohistochemical subtype | |||
| HR-HER2- | 442 (22.9%) | 442 (79.6%) | 0 (0%) |
| HR-HER2+ | 88 (4.6%) | 88 (15.9%) | 0 (0%) |
| HR+HER2- | 1167 (60.4%) | 22 (4.0%) | 1145 (83.2%) |
| HR+HER2+ | 235 (12.2%) | 3 (0.5%) | 231 (16.8%) |
| Missing | 332 (14.7%) | 4 (0.7%) | 3 (0.2%) |
| Surgery | |||
| Lumpectomy | 1410 (62.3%) | 352 (63.0%) | 881 (63.9%) |
| Mastectomy | 811 (35.8%) | 196 (35.1%) | 474 (34.4%) |
| Surgery NNO | 43 (1.9%) | 11 (2.0%) | 24 (1.7%) |
| Radiotherapy | |||
| No | 754 (33.3%) | 179 (32.0%) | 442 (32.1%) |
| Yes | 1510 (66.7%) | 380 (68.0%) | 937 (67.9%) |
PRD.final %>%
filter(delta == 0 & time < 10) %>%
nrow(.)## [1] 27#27 women lost to FUP after 10 yearsMultiple impuation
ER.num is a continuous variable. ER.cat.final was created based on the value of ER.num. If ER.num < 10%, ER.cat.final is negative, if ER.num >= 10%, ER.cat.final is positive. Besides, a binary ER status (10% cut-off) according to the registry from IKNL was used if ER.num was missing. However, in this project, we want to use 1% cut-off, which is widely used internationally. Therefore, we impute ER.num with a condition: if ER.cat.final is negative, then the imputed ER.num should be between 0 and 9; if ER.cat.final is positive, then the imputed ER.num should be between 10 and 100. This is achieved by post argument in the mice function.
We select age, incidence year, ER(continuous), PR(continuous), HER2, TILs, tumor grade, tumor size, pushing border, lymphovascular invasion, histological subtypes, radiotherapy, surgery, cumulative baseline hazard, and death to impute the missing values.
Missing patern
columns<-c("palga_nr","T_LEEFT","incyear","ER.num","ER.10","PR.num","PR.10","her2","HER2.cat",
"TILs.cat4","Grade","grade", "size","tumorsize.cat4","push.border","LVI",
"HISTOLOGICAL_SUBTYPErec","Radiotherapy","Surgery","H.tilde", "delta","time")
## Multiple imputation
data <- PRD[,columns] # select relevant columns
aggr(data, col=c('navyblue','red'), numbers=TRUE, sortVars=TRUE, labels=names(data), cex.axis=.7, gap=3, ylab=c("Histogram of missing","Pattern"))
##
## Variables sorted by number of missings:
## Variable Count
## TILs.cat4 0.15485564
## push.border 0.15398075
## PR.num 0.14479440
## size 0.14435696
## ER.num 0.14348206
## her2 0.14348206
## HER2.cat 0.14348206
## LVI 0.12467192
## Grade 0.07874016
## grade 0.07874016
## HISTOLOGICAL_SUBTYPErec 0.01574803
## palga_nr 0.00000000
## T_LEEFT 0.00000000
## incyear 0.00000000
## ER.10 0.00000000
## PR.10 0.00000000
## tumorsize.cat4 0.00000000
## Radiotherapy 0.00000000
## Surgery 0.00000000
## H.tilde 0.00000000
## delta 0.00000000
## time 0.00000000Conditions for imputation
ini <- mice(data, maxit=0, vis="monotone",print=FALSE) ## Warning: Number of logged events: 4# dry run: so that we have mice component generated and we can modify them
meth <- ini$meth
pred <- quickpred(data,
exclude=c("palga_nr","ER.10","PR.10","HER2.cat","Grade", "tumorsize.cat4", "time"),
include = c("T_LEEFT", "incyear", "ER.num","PR.num", "her2", "size", "TILs.cat4", "grade",
"HISTOLOGICAL_SUBTYPErec","LVI","push.border","Radiotherapy","Surgery",
"delta", "H.tilde"),
minpuc = 0.2, mincor = 0.125)
pred["ER.10",] <- 0
pred[,"ER.10"] <- 0
pred["PR.10",] <- 0
pred[,"PR.10"] <- 0
pred[,"HER2.cat"] <- 0
pred["HER2.cat",] <-0
pred[,"Grade"] <- 0
pred["Grade",] <- 0
pred[,"tumorsize.cat4"] <- 0
pred["tumorsize.cat4",] <- 0
pred## palga_nr T_LEEFT incyear ER.num ER.10 PR.num PR.10 her2
## palga_nr 0 0 0 0 0 0 0 0
## T_LEEFT 0 0 0 0 0 0 0 0
## incyear 0 0 0 0 0 0 0 0
## ER.num 0 1 1 0 0 1 0 1
## ER.10 0 0 0 0 0 0 0 0
## PR.num 0 1 1 1 0 0 0 1
## PR.10 0 0 0 0 0 0 0 0
## her2 0 1 1 1 0 1 0 0
## HER2.cat 0 0 0 0 0 0 0 0
## TILs.cat4 0 1 1 1 0 1 0 1
## Grade 0 0 0 0 0 0 0 0
## grade 0 1 1 1 0 1 0 1
## size 0 1 1 1 0 1 0 1
## tumorsize.cat4 0 0 0 0 0 0 0 0
## push.border 0 1 1 1 0 1 0 1
## LVI 0 1 1 1 0 1 0 1
## HISTOLOGICAL_SUBTYPErec 0 1 1 1 0 1 0 1
## Radiotherapy 0 0 0 0 0 0 0 0
## Surgery 0 0 0 0 0 0 0 0
## H.tilde 0 0 0 0 0 0 0 0
## delta 0 0 0 0 0 0 0 0
## time 0 0 0 0 0 0 0 0
## HER2.cat TILs.cat4 Grade grade size tumorsize.cat4
## palga_nr 0 0 0 0 0 0
## T_LEEFT 0 0 0 0 0 0
## incyear 0 0 0 0 0 0
## ER.num 0 1 0 1 1 0
## ER.10 0 0 0 0 0 0
## PR.num 0 1 0 1 1 0
## PR.10 0 0 0 0 0 0
## her2 0 1 0 1 1 0
## HER2.cat 0 0 0 0 0 0
## TILs.cat4 0 0 0 1 1 0
## Grade 0 0 0 0 0 0
## grade 0 1 0 0 1 0
## size 0 1 0 1 0 0
## tumorsize.cat4 0 0 0 0 0 0
## push.border 0 1 0 1 1 0
## LVI 0 1 0 1 1 0
## HISTOLOGICAL_SUBTYPErec 0 1 0 1 1 0
## Radiotherapy 0 0 0 0 0 0
## Surgery 0 0 0 0 0 0
## H.tilde 0 0 0 0 0 0
## delta 0 0 0 0 0 0
## time 0 0 0 0 0 0
## push.border LVI HISTOLOGICAL_SUBTYPErec Radiotherapy
## palga_nr 0 0 0 0
## T_LEEFT 0 0 0 0
## incyear 0 0 0 0
## ER.num 1 1 1 1
## ER.10 0 0 0 0
## PR.num 1 1 1 1
## PR.10 0 0 0 0
## her2 1 1 1 1
## HER2.cat 0 0 0 0
## TILs.cat4 1 1 1 1
## Grade 0 0 0 0
## grade 1 1 1 1
## size 1 1 1 1
## tumorsize.cat4 0 0 0 0
## push.border 0 1 1 1
## LVI 1 0 1 1
## HISTOLOGICAL_SUBTYPErec 1 1 0 1
## Radiotherapy 0 0 0 0
## Surgery 0 0 0 0
## H.tilde 0 0 0 0
## delta 0 0 0 0
## time 0 0 0 0
## Surgery H.tilde delta time
## palga_nr 0 0 0 0
## T_LEEFT 0 0 0 0
## incyear 0 0 0 0
## ER.num 1 1 1 0
## ER.10 0 0 0 0
## PR.num 1 1 1 0
## PR.10 0 0 0 0
## her2 1 1 1 0
## HER2.cat 0 0 0 0
## TILs.cat4 1 1 1 0
## Grade 0 0 0 0
## grade 1 1 1 0
## size 1 1 1 0
## tumorsize.cat4 0 0 0 0
## push.border 1 1 1 0
## LVI 1 1 1 0
## HISTOLOGICAL_SUBTYPErec 1 1 1 0
## Radiotherapy 0 0 0 0
## Surgery 0 0 0 0
## H.tilde 0 0 0 0
## delta 0 0 0 0
## time 0 0 0 0#these are ordered variable, I need to specify:
meth[c("TILs.cat4", "push.border", "grade")] = "polr" #proportional odds model for ordered data
post<-ini$post
post["ER.num"] <-"imp[[j]][data$ER.10[!r[,j]]==0, i] <- squeeze(imp[[j]][data$ER.10[!r[,j]]==0, i], c(0,9));
imp[[j]][data$ER.10[!r[,j]]==1, i] <- squeeze(imp[[j]][data$ER.10[!r[,j]]==1, i], c(10,100))"
post["PR.num"] <-"imp[[j]][data$PR.10[!r[,j]]==0, i] <- squeeze(imp[[j]][data$PR.10[!r[,j]]==0, i], c(0,9));
imp[[j]][data$PR.10[!r[,j]]==1, i] <- squeeze(imp[[j]][data$PR.10[!r[,j]]==1, i], c(10,100))"
post["size"] <-"imp[[j]][data$tumorsize.cat4[!r[,j]]==1, i] <- squeeze(imp[[j]][data$tumorsize.cat4[!r[,j]]==1, i], c(0,20));
imp[[j]][data$tumorsize.cat4[!r[,j]]==2, i] <- squeeze(imp[[j]][data$tumorsize.cat4[!r[,j]]==2, i], c(21,50));
imp[[j]][data$tumorsize.cat4[!r[,j]]==3, i] <- squeeze(imp[[j]][data$tumorsize.cat4[!r[,j]]==3, i], c(50,200))"Imputation
## Impute
seed <- 11200 # to reproduce results
impute <- mice(data=data, m=50, seed=seed, meth=meth, pred=pred, maxit=20, post = post, vis="monotone")##
## iter imp variable
## 1 1 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 16 11 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 16 12 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 17 1 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 17 2 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 17 3 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 17 50 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 1 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 2 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 3 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 4 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 5 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 6 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 7 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 8 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 9 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 10 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 11 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 12 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 13 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 14 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 15 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 18 19 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 18 25 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 18 40 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 18 44 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 45 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 46 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 47 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 18 49 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 18 50 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 1 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 2 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 3 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 19 13 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 19 15 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 19 18 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 19 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 19 25 grade LVI ER.num her2 size PR.num push.border TILs.cat4
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## 19 38 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 39 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 40 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 41 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 42 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 43 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 44 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 45 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 46 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 47 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 48 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 49 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 19 50 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 1 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 2 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 3 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 4 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 5 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 6 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 7 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 8 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 9 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 10 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 11 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 12 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 13 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 14 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 15 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 16 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 17 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 18 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 19 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 20 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 21 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 22 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 23 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 24 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 25 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 26 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 27 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 28 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 29 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 30 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 31 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 32 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 33 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 34 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 35 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 36 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 37 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 38 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 39 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 40 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 41 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 42 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 43 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 44 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 45 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 46 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 47 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 48 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 49 grade LVI ER.num her2 size PR.num push.border TILs.cat4
## 20 50 grade LVI ER.num her2 size PR.num push.border TILs.cat4## Warning: Number of logged events: 2dataimpute <- mice::complete(impute, "long", FALSE)#False indicated that I don't want the orignial dataset included into dataimpute
#check conditional imputation
dataimpute %>%
group_by(tumorsize.cat4) %>%
summarise(max = max(size), min = min(size))## # A tibble: 5 × 3
## tumorsize.cat4 max min
## <dbl> <dbl> <dbl>
## 1 1 20 1
## 2 2 50 21
## 3 3 140 50
## 4 4 63 2
## 5 999 60 1dataimpute %>%
group_by(ER.10) %>%
summarise(max = max(ER.num), min = min(ER.num))## # A tibble: 3 × 3
## ER.10 max min
## <dbl> <dbl> <dbl>
## 1 0 9 0
## 2 1 100 10
## 3 999 100 0dataimpute %>%
group_by(PR.10) %>%
summarise(max = max(PR.num), min = min(PR.num))## # A tibble: 3 × 3
## PR.10 max min
## <dbl> <dbl> <dbl>
## 1 0 9 0
## 2 1 100 10
## 3 999 100 0#check if it is converged
plot(impute, c("ER.num","PR.num","her2","grade","TILs.cat4","push.border","LVI","size"))
#the plot looks OK
densityplot(impute,data =~ size)
densityplot(impute,data =~ PR.num)
densityplot(impute,data =~ ER.num)
densityplot(impute,data =~ her2)
densityplot(impute,data =~ TILs.cat4)
densityplot(impute,data =~ grade)
densityplot(impute,data =~ push.border)
densityplot(impute,data =~ LVI)
### Percentage after MI
labels.table <- list(
variables = list(T_LEEFT = "Age (years)",
er = "ER status",
pr = "PR status",
her2 = "HER2 status",
size = "Tumor size(mm)",
grade = "Tumor grade",
LVI = "LVI",
BCsubtype = "Immunohistochemical subtype",
Surgery.cat3 = "Surgery",
Radiotherapy = "Radiotherapy"),
groups = list("", "ER stutus"))
dataimpute.final <- dataimpute %>%
filter(T_LEEFT >= 25) %>%
mutate(er = factor(ifelse(ER.num == 0, 0,
ifelse(ER.num > 0, 1, NA))),
pr = factor(ifelse(PR.num == 0, 0,
ifelse(PR.num > 0, 1, NA))),
her2 = factor(her2),
grade = factor(grade),
LVI = factor(LVI),
TILs.cat4 = factor(TILs.cat4),
Surgery.cat3 = factor(ifelse(str_detect(Surgery, "lumpectomy"), "Lumpectomy",
ifelse(str_detect(Surgery, "mastectomy"), "Mastectomy", "Surgery NNO")))) %>%
mutate(BCsubtype = ifelse((er == 1| pr == 1) & her2 == 0, "HR+HER2-",
ifelse((er == 1| pr == 1) & her2 == 1, "HR+HER2+",
ifelse(er == 0 & pr == 0 & her2 == 1, "HR-HER2+",
ifelse(er == 0 & pr == 0 & her2 == 0, "HR-HER2-", NA)))))
strata.table <- c(list(Total = dataimpute.final), split(dataimpute.final, dataimpute.final$er))
table1(strata.table, labels.table, groupspan=c(1, 2),
render.continuous = "MEDIAN (Q1, Q3)",
render.categorical = "PCT%")ER stutus |
|||
|---|---|---|---|
| Total (N=113200) |
0 (N=32432) |
1 (N=80768) |
|
| Age (years) | 36.0 (33.0, 38.0) | 35.0 (32.0, 38.0) | 36.0 (34.0, 38.0) |
| ER status | |||
| 0 | 28.7% | 100% | 0% |
| 1 | 71.3% | 0% | 100% |
| PR status | |||
| 0 | 36.5% | 94.4% | 13.3% |
| 1 | 63.5% | 5.6% | 86.7% |
| HER2 status | |||
| 0 | 83.2% | 82.9% | 83.3% |
| 1 | 16.8% | 17.1% | 16.7% |
| Tumor size(mm) | 17.0 (12.0, 21.0) | 20.0 (15.0, 25.0) | 15.0 (12.0, 20.0) |
| Tumor grade | |||
| 1 | 17.7% | 1.5% | 24.2% |
| 2 | 38.5% | 16.5% | 47.3% |
| 3 | 43.8% | 82.0% | 28.5% |
| LVI | |||
| 0 | 82.4% | 86.5% | 80.8% |
| 1 | 17.6% | 13.5% | 19.2% |
| Immunohistochemical subtype | |||
| HR-HER2- | 22.3% | 78.0% | 0% |
| HR-HER2+ | 4.7% | 16.4% | 0% |
| HR+HER2- | 60.8% | 4.9% | 83.3% |
| HR+HER2+ | 12.1% | 0.7% | 16.7% |
| Surgery | |||
| Lumpectomy | 62.3% | 61.8% | 62.5% |
| Mastectomy | 35.8% | 36.2% | 35.7% |
| Surgery NNO | 1.9% | 2.0% | 1.8% |
| Radiotherapy | |||
| No | 33.3% | 33.3% | 33.3% |
| Yes | 66.7% | 66.7% | 66.7% |
Nr.ER <- as.data.frame(table(dataimpute.final$.imp, dataimpute.final$er))
median(Nr.ER$Freq[which(Nr.ER$Var2 == 0)])#649.5## [1] 649.5median(Nr.ER$Freq[which(Nr.ER$Var2 == 1)])#1614.5## [1] 1614.5Nr.subtype <- as.data.frame(table(dataimpute.final$.imp, dataimpute.final$BCsubtype))
median(Nr.subtype$Freq[which(Nr.subtype$Var2 == "HR+HER2-")])#1376## [1] 1376median(Nr.subtype$Freq[which(Nr.subtype$Var2 == "HR+HER2+")])#274## [1] 274median(Nr.subtype$Freq[which(Nr.subtype$Var2 == "HR-HER2+")])#106## [1] 106median(Nr.subtype$Freq[which(Nr.subtype$Var2 == "HR-HER2-")])#506## [1] 505.5PREDICT v2.2
Prepare for running PREDICT script
PREDICT needs information on age, ER, PR, HER2, tumor grade, tumor size, nodes, ki67, screening detected or not, and adjuvant treatment. In the PARADIGM, all patients had negative nodes, therefore, nodes are zero; all patients were diagnosed under 40, therefore, all patients were detected from clinic; all patients are systemic therapy-naive, therefore, treatment should be negative.
dataimpute.v22 <- dataimpute %>%
mutate(er = factor(ifelse(ER.num == 0, 0,
ifelse(ER.num > 0, 1, NA))),
pr = factor(ifelse(PR.num == 0, 0,
ifelse(PR.num > 0, 1, NA))),
age.start = T_LEEFT,
nodes = 0,
screen = 0,
ki67 = 9,
generation = 0,
horm = 0,
traz = 0,
bis = 0,
timeyr = time,
Surgery.cat3 = factor(ifelse(str_detect(Surgery, "lumpectomy"), "Lumpectomy",
ifelse(str_detect(Surgery, "mastectomy"), "Mastectomy", "Surgery NNO")))) %>%
dplyr::select(- time) Run PREDICT V2.2
Note that PREDICTv2.2 published on https://github.com/gmp26/v22/blob/master/R/v22_model.R was slightly different. 1. PREDICTv2.2 allows missing values in grade (if missing, impute missingness with 2.13) and screening (if missing, impute missingness with 0.204). In my script, grade is not allowed to be missing, the same as the PREDICT published on their website. For screening, we expect very few women in our cohort who would undergo screening, as they were all under 40 years at diagnosis. Screening for BRCA1/2 carriers was not yet inplemented before 2000. 2. PREDICTv2.2 on the github added several codes about radiotherapy. However, these codes are currently disabled thus not being used right now. 3. PREDICTv2.2 on the github included systemic treatment. I deleted these part because all women in our cohort were adjuvant treatment-naive.
#calculate PI and predicted outcomes based on the PREDICT version 2.2
names(dataimpute.v22)## [1] ".imp" ".id"
## [3] "palga_nr" "T_LEEFT"
## [5] "incyear" "ER.num"
## [7] "ER.10" "PR.num"
## [9] "PR.10" "her2"
## [11] "HER2.cat" "TILs.cat4"
## [13] "Grade" "grade"
## [15] "size" "tumorsize.cat4"
## [17] "push.border" "LVI"
## [19] "HISTOLOGICAL_SUBTYPErec" "Radiotherapy"
## [21] "Surgery" "H.tilde"
## [23] "delta" "er"
## [25] "pr" "age.start"
## [27] "nodes" "screen"
## [29] "ki67" "generation"
## [31] "horm" "traz"
## [33] "bis" "timeyr"
## [35] "Surgery.cat3"for (n in 1:nrow(dataimpute.v22)) {
# Input case characteristics
age.start <- dataimpute.v22$age.start[n] #25 to 85
screen <- dataimpute.v22$screen[n] # Clinically detected = 0, screen detected = 1
size <- dataimpute.v22$size[n] # Tumour size mm
grade <- dataimpute.v22$grade[n] # Tumour grade
nodes <- dataimpute.v22$nodes[n] # Number positive nodes. Nodal micrometastases = 0.5
er <- dataimpute.v22$er[n] # ER+ = 1, ER- = 0
her2 <- dataimpute.v22$her2[n] # HER2+ = 1, HER2- = 0, missing = 9
ki67 <- dataimpute.v22$ki67[n] # KI67+ = 1, KI67- = 0, missing = 9
generation <- dataimpute.v22$generation[n] # Chemo generation 0, 2 or 3 only
horm <- dataimpute.v22$horm[n] # Hormone therapy Yes = 1, no = 0
traz <- dataimpute.v22$traz[n] # Trastuzumab therapy Yes = 1, no = 0
bis <- dataimpute.v22$bis[n] # Bisphosphonate therapy Yes = 1, no = 0
# Grade variable for ER neg
grade.val <- ifelse(er==1, grade, ifelse(grade==1, 0, 1))
# Generate the coefficients
age.mfp.1 <- ifelse(er==1, (age.start/10)^-2-.0287449295, age.start-56.3254902)
age.beta.1 <- ifelse(er==1, 34.53642, 0.0089827)
age.mfp.2 <- ifelse(er==1, (age.start/10)^-2*log(age.start/10)-.0510121013, 0)
age.beta.2 <- ifelse(er==1, -34.20342, 0)
size.mfp <- ifelse(er==1, log(size/100)+1.545233938, (size/100)^.5-.5090456276)
size.beta <- ifelse(er==1, 0.7530729, 2.093446)
nodes.mfp <- ifelse(er==1,log((nodes+1)/10)+1.387566896,
log((nodes+1)/10)+1.086916249)
nodes.beta <- ifelse(er==1, 0.7060723, .6260541)
grade.beta <- ifelse(er==1, 0.746655, 1.129091)
screen.beta <- ifelse(er==1, -0.22763366, 0)
her2.beta <- ifelse(her2==1, 0.2413,
ifelse(her2==0, -0.0762, 0))
ki67.beta <- ifelse(ki67==1 & er==1, 0.14904,
ifelse(ki67==0 & er==1, -0.1133, 0))
# Calculate the other and breast mortality indicies
# Other mortality prognostic index (mi)
mi <- 0.0698252*((age.start/10)^2-34.23391957)
# Breast cancer mortality prognostic index (v22)
pi <- age.beta.1*age.mfp.1 + age.beta.2*age.mfp.2 + size.beta*size.mfp +
nodes.beta*nodes.mfp + grade.beta*grade.val + screen.beta*screen +
her2.beta + ki67.beta
# Non breast cancer mortality
# Generate cumulative baseline other mortality
time<-c(5,10,15)
base.m.cum.oth <- exp(-6.052919 + (1.079863*log(time)) + (.3255321*time^.5))
# Generate cumulative survival non-breast mortality
#YW: cumulative non-breast cancer survival
s.cum.oth <- exp(-exp(mi)*base.m.cum.oth)
# Generate annual survival from cumulative survival
#YW: cumulative non-breast cancer mortality
m.cum.oth <- 1 - s.cum.oth
# Breast cancer specific mortality
# Generate cumulative baseline breast mortality
if (er==1) {
base.m.cum.br <- exp(0.7424402 - 7.527762/time^.5 - 1.812513*log(time)/time^.5)
} else { base.m.cum.br <- exp(-1.156036 + 0.4707332/time^2 - 3.51355/time)
}
# Calculate the cumulative breast cancer survival
s.cum.br <- exp(-exp(pi)*base.m.cum.br)
m.cum.br <- 1 - s.cum.br
# All cause mortality
m.cum.all <- 1 - s.cum.oth*s.cum.br
#s.cum.all <- 100-100*m.cum.all
# Proportion of all cause mortality that is breast cancer
prop.br <- m.cum.br/(m.cum.br+m.cum.oth)
prop.oth <- m.cum.oth/(m.cum.br+m.cum.oth)
# Predicted cumulative breast specific mortality
pred.m.br <- prop.br*m.cum.all
# Predicted cumulative non-breast cancer mortality
pred.m.oth <- prop.oth*m.cum.all
# Predicted cumulative all-cause mortality
pred.all <- pred.m.br + pred.m.oth #pred.all=m.cum.all
#dataimpute$br[n] <- pred.m.br # Predicted breast cancer mortality
#dataimpute$oth[n] <- pred.m.oth # Predicted non-breast cancer mortality
# Predicted all cause mortality
dataimpute.v22$s.cum.br5[n] <- s.cum.br[1]
dataimpute.v22$s.cum.br10[n] <- s.cum.br[2]
dataimpute.v22$s.cum.br15[n] <- s.cum.br[3]
dataimpute.v22$s.cum.oth5[n] <- s.cum.oth[1]
dataimpute.v22$s.cum.oth10[n] <- s.cum.oth[2]
dataimpute.v22$s.cum.oth15[n] <- s.cum.oth[3]
dataimpute.v22$all5[n] <- pred.all[1]
dataimpute.v22$all10[n] <- pred.all[2]
dataimpute.v22$all15[n] <- pred.all[3]
#prognostic index, this is not related to time
dataimpute.v22$pi[n] <- pi #larger pi is associated with higher risks of events
}
#calculate the predicted 10 years survival using PREDICT! v2.2:
dataimpute.v22<- dataimpute.v22 %>%
mutate(predsurv5 = 1 - all5,
predsurv10 = 1 - all10,
predsurv15 = 1 - all15)PREDICT v2.3
Prepare for running PREDICT script
dataimpute.v23 <- dataimpute %>%
mutate(er = factor(ifelse(ER.num == 0, 0,
ifelse(ER.num > 0, 1, NA))),
pr = factor(ifelse(PR.num == 0, 0,
ifelse(PR.num > 0, 1, NA))),
age.start = T_LEEFT,
nodes = 0,
screen = 0,
ki67 = 9,
generation = 0,
horm = 0,
traz = 0,
bis = 0,
timeyr = time,
Surgery.cat3 = factor(ifelse(str_detect(Surgery, "lumpectomy"), "Lumpectomy",
ifelse(str_detect(Surgery, "mastectomy"), "Mastectomy", "Surgery NNO")))) %>%
dplyr::select(- time) Run PREDICT V2.3
PREDICT v2.3 was not yet published. The information on the predictor PR was derived from the manuscript from Grootes et al. 2022.
for (n in 1:nrow(dataimpute.v23)) {
# Input case characteristics
age.start <- dataimpute.v23$age.start[n]
screen <- dataimpute.v23$screen[n] # Clinically detected = 0, screen detected = 1
size <- dataimpute.v23$size[n] # Tumour size mm
grade <- dataimpute.v23$grade[n] # Tumour grade
nodes <- dataimpute.v23$nodes[n] # Number positive nodes. Nodal micrometastases = 0.5
er <- dataimpute.v23$er[n] # ER+ = 1, ER- = 0
her2 <- dataimpute.v23$her2[n] # HER2+ = 1, HER2- = 0, missing = 9
ki67 <- dataimpute.v23$ki67[n] # KI67+ = 1, KI67- = 0, missing = 9
generation <- dataimpute.v23$generation[n] # Chemo generation 0, 2 or 3 only
horm <- dataimpute.v23$horm[n] # Hormone therapy Yes = 1, no = 0
traz <- dataimpute.v23$traz[n] # Trastuzumab therapy Yes = 1, no = 0
bis <- dataimpute.v23$bis[n] # Bisphosphonate therapy Yes = 1, no = 0
pr <- dataimpute.v23$pr[n]
# Grade variable for ER neg
grade.val <- ifelse(er==1, grade, ifelse(grade==1, 0, 1))
# Generate the coefficients
age.mfp.1 <- ifelse(er==1, (age.start/10)^-2-.0287449295, age.start-56.3254902)
age.beta.1 <- ifelse(er==1, 34.53642, 0.0089827)
age.mfp.2 <- ifelse(er==1, (age.start/10)^-2*log(age.start/10)-.0510121013, 0)
age.beta.2 <- ifelse(er==1, -34.20342, 0)
size.mfp <- ifelse(er==1, log(size/100)+1.545233938, (size/100)^.5-.5090456276)
size.beta <- ifelse(er==1, 0.7530729, 2.093446)
nodes.mfp <- ifelse(er==1,log((nodes+1)/10)+1.387566896,
log((nodes+1)/10)+1.086916249)
nodes.beta <- ifelse(er==1, 0.7060723, .6260541)
grade.beta <- ifelse(er==1, 0.746655, 1.129091)
screen.beta <- ifelse(er==1, -0.22763366, 0)
her2.beta <- ifelse(her2==1, 0.2413,
ifelse(her2==0, -0.0762, 0))
ki67.beta <- ifelse(ki67==1 & er==1, 0.14904,
ifelse(ki67==0 & er==1, -0.1133, 0))
pr.beta <- ifelse(pr == 1 & er == 1, -0.0618754,
ifelse(pr == 0 & er == 1, 0.2623643,
ifelse(pr == 1 & er == 0, -0.2231436, 0.0295588)))
# Other mortality prognostic index (mi)
mi <- 0.0698252*((age.start/10)^2-34.23391957)
# Breast cancer mortality prognostic index (pi)
pi <- age.beta.1*age.mfp.1 + age.beta.2*age.mfp.2 + size.beta*size.mfp +
nodes.beta*nodes.mfp + grade.beta*grade.val + screen.beta*screen +
her2.beta + ki67.beta + pr.beta
# Non breast cancer mortality
# Generate cumulative baseline other mortality
time<-c(5,10,15)
base.m.cum.oth <- exp(-6.052919 + (1.079863*log(time)) + (.3255321*time^.5))
# Generate cumulative survival non-breast mortality
#YW: cumulative non-breast cancer survival
s.cum.oth <- exp(-exp(mi)*base.m.cum.oth)
# Generate annual survival from cumulative survival
#YW: cumulative non-breast cancer mortality
m.cum.oth <- 1 - s.cum.oth
# Breast cancer specific mortality
# Generate cumulative baseline breast mortality
if (er==1) {
base.m.cum.br <- exp(0.7424402 - 7.527762/time^.5 - 1.812513*log(time)/time^.5)
} else { base.m.cum.br <- exp(-1.156036 + 0.4707332/time^2 - 3.51355/time)
}
# Calculate the cumulative breast cancer survival
s.cum.br <- exp(-exp(pi)*base.m.cum.br)
m.cum.br <- 1 - s.cum.br
# All cause mortality
m.cum.all <- 1 - s.cum.oth*s.cum.br
s.cum.all <- 100-100*m.cum.all
# Proportion of all cause mortality that is breast cancer
prop.br <- m.cum.br/(m.cum.br+m.cum.oth)
prop.oth <- m.cum.oth/(m.cum.br+m.cum.oth)
# Predicted cumulative breast specific mortality
pred.m.br <- prop.br*m.cum.all
# Predicted cumulative non-breast cancer mortality
pred.m.oth <- prop.oth*m.cum.all
# Predicted cumulative all-cause mortality
pred.all <- pred.m.br + pred.m.oth #pred.all=m.cum.all
#dataimpute$br[n] <- pred.m.br # Predicted breast cancer mortality
#dataimpute$oth[n] <- pred.m.oth # Predicted non-breast cancer mortality
# Predicted all cause mortality
dataimpute.v23$s.cum.br5[n] <- s.cum.br[1]
dataimpute.v23$s.cum.br10[n] <- s.cum.br[2]
dataimpute.v23$s.cum.br15[n] <- s.cum.br[3]
dataimpute.v23$s.cum.oth5[n] <- s.cum.oth[1]
dataimpute.v23$s.cum.oth10[n] <- s.cum.oth[2]
dataimpute.v23$s.cum.oth15[n] <- s.cum.oth[3]
dataimpute.v23$all5[n] <- pred.all[1]
dataimpute.v23$all10[n] <- pred.all[2]
dataimpute.v23$all15[n] <- pred.all[3]
#prognostic index, this is not related to time
dataimpute.v23$pi[n] <- pi #larger pi is associated with higher risks of events
}
#calculate the predicted 10 years survival using PREDICT! v.2.1:
dataimpute.v23<- dataimpute.v23 %>%
mutate(predsurv5 = 1 - all5,
predsurv10 = 1 - all10,
predsurv15 = 1 - all15)Session information
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] table1_1.4.3 VIM_6.2.2 colorspace_2.0-3 MASS_7.3-55
## [5] forcats_0.5.2 stringr_1.5.0 dplyr_1.1.1 purrr_1.0.0
## [9] tidyr_1.2.1 tibble_3.2.1 ggplot2_3.4.1 tidyverse_1.3.2
## [13] plyr_1.8.8 survival_3.5-5 mice_3.15.0 readr_2.1.3
## [17] readxl_1.4.1
##
## loaded via a namespace (and not attached):
## [1] fs_1.5.2 bit64_4.0.5 lubridate_1.9.0
## [4] httr_1.4.4 tools_4.1.3 backports_1.4.1
## [7] bslib_0.4.2 utf8_1.2.2 R6_2.5.1
## [10] DBI_1.1.3 nnet_7.3-17 withr_2.5.0
## [13] sp_1.6-0 tidyselect_1.2.0 bit_4.0.5
## [16] compiler_4.1.3 cli_3.5.0 rvest_1.0.3
## [19] xml2_1.3.3 sass_0.4.4 scales_1.2.1
## [22] DEoptimR_1.0-11 lmtest_0.9-40 robustbase_0.95-0
## [25] proxy_0.4-27 digest_0.6.31 rmarkdown_2.19
## [28] pkgconfig_2.0.3 htmltools_0.5.4 highr_0.10
## [31] dbplyr_2.2.1 fastmap_1.1.0 rlang_1.1.0
## [34] rstudioapi_0.14 jquerylib_0.1.4 generics_0.1.3
## [37] zoo_1.8-11 jsonlite_1.8.4 vroom_1.6.0
## [40] car_3.1-1 googlesheets4_1.0.1 magrittr_2.0.3
## [43] Formula_1.2-4 Matrix_1.5-3 Rcpp_1.0.9
## [46] munsell_0.5.0 fansi_1.0.3 abind_1.4-5
## [49] lifecycle_1.0.3 stringi_1.7.8 yaml_2.3.6
## [52] carData_3.0-5 parallel_4.1.3 crayon_1.5.2
## [55] lattice_0.20-45 haven_2.5.1 splines_4.1.3
## [58] hms_1.1.2 knitr_1.41 pillar_1.8.1
## [61] ranger_0.14.1 boot_1.3-28 reprex_2.0.2
## [64] glue_1.6.2 evaluate_0.19 data.table_1.14.6
## [67] laeken_0.5.2 modelr_0.1.10 vcd_1.4-10
## [70] vctrs_0.6.1 tzdb_0.3.0 cellranger_1.1.0
## [73] gtable_0.3.1 assertthat_0.2.1 cachem_1.0.6
## [76] xfun_0.36 broom_1.0.2 e1071_1.7-12
## [79] class_7.3-20 googledrive_2.0.0 gargle_1.2.1
## [82] timechange_0.1.1 ellipsis_0.3.2